DonorsChoose¶
DonorsChoose.org receives hundreds of thousands of project proposals each year for classroom projects in need of funding. Right now, a large number of volunteers is needed to manually screen each submission before it's approved to be posted on the DonorsChoose.org website.
Next year, DonorsChoose.org expects to receive close to 500,000 project proposals. As a result, there are three main problems they need to solve:
- How to scale current manual processes and resources to screen 500,000 projects so that they can be posted as quickly and as efficiently as possible
- How to increase the consistency of project vetting across different volunteers to improve the experience for teachers
- How to focus volunteer time on the applications that need the most assistance
The goal of the competition is to predict whether or not a DonorsChoose.org project proposal submitted by a teacher will be approved, using the text of project descriptions as well as additional metadata about the project, teacher, and school. DonorsChoose.org can then use this information to identify projects most likely to need further review before approval.
About the DonorsChoose Data Set¶
The train.csv data set provided by DonorsChoose contains the following features:
| Feature | Description |
|---|---|
project_id |
A unique identifier for the proposed project. Example: p036502 |
project_title |
Title of the project. Examples:
|
project_grade_category |
Grade level of students for which the project is targeted. One of the following enumerated values:
|
project_subject_categories |
One or more (comma-separated) subject categories for the project from the following enumerated list of values:
Examples:
|
school_state |
State where school is located (Two-letter U.S. postal code). Example: WY |
project_subject_subcategories |
One or more (comma-separated) subject subcategories for the project. Examples:
|
project_resource_summary |
An explanation of the resources needed for the project. Example:
|
project_essay_1 |
First application essay* |
project_essay_2 |
Second application essay* |
project_essay_3 |
Third application essay* |
project_essay_4 |
Fourth application essay* |
project_submitted_datetime |
Datetime when project application was submitted. Example: 2016-04-28 12:43:56.245 |
teacher_id |
A unique identifier for the teacher of the proposed project. Example: bdf8baa8fedef6bfeec7ae4ff1c15c56 |
teacher_prefix |
Teacher's title. One of the following enumerated values:
|
teacher_number_of_previously_posted_projects |
Number of project applications previously submitted by the same teacher. Example: 2 |
* See the section Notes on the Essay Data for more details about these features.
Additionally, the resources.csv data set provides more data about the resources required for each project. Each line in this file represents a resource required by a project:
| Feature | Description |
|---|---|
id |
A project_id value from the train.csv file. Example: p036502 |
description |
Desciption of the resource. Example: Tenor Saxophone Reeds, Box of 25 |
quantity |
Quantity of the resource required. Example: 3 |
price |
Price of the resource required. Example: 9.95 |
Note: Many projects require multiple resources. The id value corresponds to a project_id in train.csv, so you use it as a key to retrieve all resources needed for a project:
The data set contains the following label (the value you will attempt to predict):
| Label | Description |
|---|---|
project_is_approved |
A binary flag indicating whether DonorsChoose approved the project. A value of 0 indicates the project was not approved, and a value of 1 indicates the project was approved. |
Notes on the Essay Data¶
-
Prior to May 17, 2016, the prompts for the essays were as follows:
- __project_essay_1:__ "Introduce us to your classroom"
- __project_essay_2:__ "Tell us more about your students"
- __project_essay_3:__ "Describe how your students will use the materials you're requesting"
- __project_essay_4:__ "Close by sharing why your project will make a difference"
-
Starting on May 17, 2016, the number of essays was reduced from 4 to 2, and the prompts for the first 2 essays were changed to the following:
- __project_essay_1:__ "Describe your students: What makes your students special? Specific details about their background, your neighborhood, and your school are all helpful."
- __project_essay_2:__ "About your project: How will these materials make a difference in your students' learning and improve their school lives?"
For all projects with project_submitted_datetime of 2016-05-17 and later, the values of project_essay_3 and project_essay_4 will be NaN.
In [2]:
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
import sqlite3
import pandas as pd
import numpy as np
import nltk
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
from nltk.stem.porter import PorterStemmer
import re
# Tutorial about Python regular expressions: https://pymotw.com/2/re/
import string
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem.wordnet import WordNetLemmatizer
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pickle
from tqdm import tqdm
import os
from plotly import plotly
import plotly.offline as offline
import plotly.graph_objs as go
offline.init_notebook_mode()
from collections import Counter
1.1 Reading Data¶
In [3]:
# #from google.colab import files
# #uploaded = files.upload()
# #import io
# #project_data = pd.read_csv(io.BytesIO(uploaded['train_data.csv']))
# #resource_data = pd.read_csv(io.BytesIO(uploaded['resources.csv']))
# # Code to read csv file into Colaboratory:
# !pip install -U -q PyDrive
# from pydrive.auth import GoogleAuth
# from pydrive.drive import GoogleDrive
# from google.colab import auth
# from oauth2client.client import GoogleCredentials
# # Authenticate and create the PyDrive client.
# auth.authenticate_user()
# gauth = GoogleAuth()
# gauth.credentials = GoogleCredentials.get_application_default()
# drive = GoogleDrive(gauth)
# link = 'https://drive.google.com/open?id=1IUZ8vHvc91SAQ6H-F0_D1t-7jtINkuT4' # The shareable link
# fluff, id = link.split('=')
# print (id) # Verify that you have everything after '='
# downloaded = drive.CreateFile({'id':id})
# downloaded.GetContentFile('train_data.csv')
# project_data = pd.read_csv('train_data.csv')
project_data = pd.read_csv('train_data.csv')
resource_data = pd.read_csv('resources.csv')
In [4]:
# !pip install -U -q PyDrive
# from pydrive.auth import GoogleAuth
# from pydrive.drive import GoogleDrive
# from google.colab import auth
# from oauth2client.client import GoogleCredentials
# # Authenticate and create the PyDrive client.
# auth.authenticate_user()
# gauth = GoogleAuth()
# gauth.credentials = GoogleCredentials.get_application_default()
# drive = GoogleDrive(gauth)
# link = 'https://drive.google.com/open?id=1KW8emLxl0ez6yMT2MXy2Hq7pFDWYUwZJ' # The shareable link
# fluff, id = link.split('=')
# print (id) # Verify that you have everything after '='
# downloaded = drive.CreateFile({'id':id})
# downloaded.GetContentFile('resources.csv')
# resource_data = pd.read_csv('resources.csv')
In [5]:
print("Number of data points in train data", project_data.shape)
print('-'*50)
print("The attributes of data :", project_data.columns.values)
In [6]:
print("Number of data points in train data", resource_data.shape)
print(resource_data.columns.values)
resource_data.head(2)
Out[6]:
1.2 Data Analysis¶
In [7]:
# PROVIDE CITATIONS TO YOUR CODE IF YOU TAKE IT FROM ANOTHER WEBSITE.
# https://matplotlib.org/gallery/pie_and_polar_charts/pie_and_donut_labels.html#sphx-glr-gallery-pie-and-polar-charts-pie-and-donut-labels-py
y_value_counts = project_data['project_is_approved'].value_counts()
print("Number of projects thar are approved for funding ", y_value_counts[1], ", (", (y_value_counts[1]/(y_value_counts[1]+y_value_counts[0]))*100,"%)")
print("Number of projects thar are not approved for funding ", y_value_counts[0], ", (", (y_value_counts[0]/(y_value_counts[1]+y_value_counts[0]))*100,"%)")
fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect="equal"))
recipe = ["Accepted", "Not Accepted"]
data = [y_value_counts[1], y_value_counts[0]]
wedges, texts = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
kw = dict(xycoords='data', textcoords='data', arrowprops=dict(arrowstyle="-"),
bbox=bbox_props, zorder=0, va="center")
for i, p in enumerate(wedges):
ang = (p.theta2 - p.theta1)/2. + p.theta1
y = np.sin(np.deg2rad(ang))
x = np.cos(np.deg2rad(ang))
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
connectionstyle = "angle,angleA=0,angleB={}".format(ang)
kw["arrowprops"].update({"connectionstyle": connectionstyle})
ax.annotate(recipe[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),
horizontalalignment=horizontalalignment, **kw)
ax.set_title("Nmber of projects that are Accepted and not accepted")
plt.show()
Observation for Pie Chat¶
- 85% of projects got approved resulting 15% of these projects are not approved too.
1.2.1 Univariate Analysis: School State¶
In [8]:
# Pandas dataframe groupby count, mean: https://stackoverflow.com/a/19385591/4084039
temp = pd.DataFrame(project_data.groupby("school_state")["project_is_approved"].apply(np.mean)).reset_index()
# if you have data which contain only 0 and 1, then the mean = percentage (think about it)
temp.columns = ['state_code', 'num_proposals']
'''# How to plot US state heatmap: https://datascience.stackexchange.com/a/9620
scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
[0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]
data = [ dict(
type='choropleth',
colorscale = scl,
autocolorscale = False,
locations = temp['state_code'],
z = temp['num_proposals'].astype(float),
locationmode = 'USA-states',
text = temp['state_code'],
marker = dict(line = dict (color = 'rgb(255,255,255)',width = 2)),
colorbar = dict(title = "% of pro")
) ]
layout = dict(
title = 'Project Proposals % of Acceptance Rate by US States',
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showlakes = True,
lakecolor = 'rgb(255, 255, 255)',
),
)
fig = go.Figure(data=data, layout=layout)
offline.iplot(fig, filename='us-map-heat-map')
'''
Out[8]:
In [9]:
# https://www.csi.cuny.edu/sites/default/files/pdf/administration/ops/2letterstabbrev.pdf
temp.sort_values(by=['num_proposals'], inplace=True)
print("States with lowest % approvals")
print(temp.head(5))
print('='*50)
print("States with highest % approvals")
print(temp.tail(5))
In [10]:
#stacked bar plots matplotlib: https://matplotlib.org/gallery/lines_bars_and_markers/bar_stacked.html
def stack_plot(data, xtick, col2='project_is_approved', col3='total'):
ind = np.arange(data.shape[0])
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, data[col3].values)
p2 = plt.bar(ind, data[col2].values)
plt.ylabel('Projects')
plt.title('Number of projects aproved vs rejected')
plt.xticks(ind, list(data[xtick].values))
plt.legend((p1[0], p2[0]), ('total', 'accepted'))
plt.show()
In [11]:
def univariate_barplots(data, col1, col2='project_is_approved', top=False):
# Count number of zeros in dataframe python: https://stackoverflow.com/a/51540521/4084039
temp = pd.DataFrame(project_data.groupby(col1)[col2].agg(lambda x: x.eq(1).sum())).reset_index()
# Pandas dataframe grouby count: https://stackoverflow.com/a/19385591/4084039
temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']
temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']
temp.sort_values(by=['total'],inplace=True, ascending=False)
if top:
temp = temp[0:top]
stack_plot(temp, xtick=col1, col2=col2, col3='total')
print(temp.head(5))
print("="*50)
print(temp.tail(5))
In [12]:
univariate_barplots(project_data, 'school_state', 'project_is_approved', False)
SUMMARY: Every state has greater than 80% success rate in approval
1.2.2 Univariate Analysis: teacher_prefix¶
In [13]:
univariate_barplots(project_data, 'teacher_prefix', 'project_is_approved' , top=False)
Observation for Univariate analysis for teacher_prefix¶
- Mrs., Ms., Mr Docotor prefix have aroung 85% approved rate .
- Surprisingly Dr. prefix people have less then 70% approve rate. Still It has very less data comparing to other.
1.2.3 Univariate Analysis: project_grade_category¶
In [14]:
univariate_barplots(project_data, 'project_grade_category', 'project_is_approved', top=False)
Observation for Univariate analysis for project_grade_category¶
- Here every grade has just below then 85% of approval rate.
- Grades PreK-2 has largest number of projects submitted and Grade 9-12 has the lowest.
1.2.4 Univariate Analysis: project_subject_categories¶
In [15]:
catogories = list(project_data['project_subject_categories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
cat_list = []
for i in catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp+=j.strip()+" " #" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_') # we are replacing the & value into
cat_list.append(temp.strip())
In [16]:
project_data['clean_categories'] = cat_list
project_data.drop(['project_subject_categories'], axis=1, inplace=True)
project_data.head(2)
Out[16]:
In [17]:
univariate_barplots(project_data, 'clean_categories', 'project_is_approved', top=20)
Observation for Univariate analysis for clean_categories¶
- Here every grade has heigest as 86% of approval rate and lowest as 81%.
- Grades Warmth Care_Hunger has largest number of projects submitted and AppliedLearning Math_Science has the lowest rate of approved project.
In [18]:
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
from collections import Counter
my_counter = Counter()
for word in project_data['clean_categories'].values:
my_counter.update(word.split())
In [19]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
cat_dict = dict(my_counter)
sorted_cat_dict = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(sorted_cat_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(sorted_cat_dict.values()))
plt.ylabel('Projects')
plt.title('% of projects aproved category wise')
plt.xticks(ind, list(sorted_cat_dict.keys()))
plt.show()
In [20]:
for i, j in sorted_cat_dict.items():
print("{:20} :{:10}".format(i,j))
Observation for Univariate analysis for clean_categories¶
- Grades Literacy_Language has largest number of projects submitted and Warmth has the lowest number of approved project.
1.2.5 Univariate Analysis: project_subject_subcategories¶
In [21]:
sub_catogories = list(project_data['project_subject_subcategories'].values)
# remove special characters from list of strings python: https://stackoverflow.com/a/47301924/4084039
# https://www.geeksforgeeks.org/removing-stop-words-nltk-python/
# https://stackoverflow.com/questions/23669024/how-to-strip-a-specific-word-from-a-string
# https://stackoverflow.com/questions/8270092/remove-all-whitespace-in-a-string-in-python
sub_cat_list = []
for i in sub_catogories:
temp = ""
# consider we have text like this "Math & Science, Warmth, Care & Hunger"
for j in i.split(','): # it will split it in three parts ["Math & Science", "Warmth", "Care & Hunger"]
if 'The' in j.split(): # this will split each of the catogory based on space "Math & Science"=> "Math","&", "Science"
j=j.replace('The','') # if we have the words "The" we are going to replace it with ''(i.e removing 'The')
j = j.replace(' ','') # we are placeing all the ' '(space) with ''(empty) ex:"Math & Science"=>"Math&Science"
temp +=j.strip()+" "#" abc ".strip() will return "abc", remove the trailing spaces
temp = temp.replace('&','_')
sub_cat_list.append(temp.strip())
In [22]:
project_data['clean_subcategories'] = sub_cat_list
project_data.drop(['project_subject_subcategories'], axis=1, inplace=True)
project_data.head(2)
Out[22]:
In [23]:
univariate_barplots(project_data, 'clean_subcategories', 'project_is_approved', top=50)
In [24]:
# count of all the words in corpus python: https://stackoverflow.com/a/22898595/4084039
from collections import Counter
my_counter = Counter()
for word in project_data['clean_subcategories'].values:
my_counter.update(word.split())
In [25]:
# dict sort by value python: https://stackoverflow.com/a/613218/4084039
sub_cat_dict = dict(my_counter)
sorted_sub_cat_dict = dict(sorted(sub_cat_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(sorted_sub_cat_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(sorted_sub_cat_dict.values()))
plt.ylabel('Projects')
plt.title('% of projects aproved state wise')
plt.xticks(ind, list(sorted_sub_cat_dict.keys()))
plt.show()
In [26]:
for i, j in sorted_sub_cat_dict.items():
print("{:20} :{:10}".format(i,j))
Observation for Univariate analysis for project_subject_subcategories¶
- Grades Literacy sub category has largest number of projects submitted and Economics has the lowest number of approved project.
1.2.6 Univariate Analysis: Text features (Title)¶
In [27]:
#How to calculate number of words in a string in DataFrame: https://stackoverflow.com/a/37483537/4084039
word_count = project_data['project_title'].str.split().apply(len).value_counts()
word_dict = dict(word_count)
word_dict = dict(sorted(word_dict.items(), key=lambda kv: kv[1]))
ind = np.arange(len(word_dict))
plt.figure(figsize=(20,5))
p1 = plt.bar(ind, list(word_dict.values()))
plt.ylabel('Numeber of projects')
plt.xlabel('Numeber words in project title')
plt.title('Words for each title of the project')
plt.xticks(ind, list(word_dict.keys()))
plt.show()
In [28]:
approved_title_word_count = project_data[project_data['project_is_approved']==1]['project_title'].str.split().apply(len)
approved_title_word_count = approved_title_word_count.values
rejected_title_word_count = project_data[project_data['project_is_approved']==0]['project_title'].str.split().apply(len)
rejected_title_word_count = rejected_title_word_count.values
In [29]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_title_word_count, rejected_title_word_count])
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Words in project title')
plt.grid()
plt.show()
In [30]:
plt.figure(figsize=(10,3))
sns.kdeplot(approved_title_word_count,label="Approved Projects", bw=0.6)
sns.kdeplot(rejected_title_word_count,label="Not Approved Projects", bw=0.6)
plt.legend()
plt.show()
Observation for Univariate analysis for project_title¶
- As per the count in Project title, Project title has as short as 4 words for the title.
- Box Plot- Seeing the box plot we can say like approved and rejected has both mean around 5 no of words.
- The project which got rejected have mostly 3 to 6 words. and Approved project tends to have 4 to 7 words.
1.2.7 Univariate Analysis: Text features (Project Essay's)¶
In [31]:
# merge two column text dataframe:
project_data["essay"] = project_data["project_essay_1"].map(str) +\
project_data["project_essay_2"].map(str) + \
project_data["project_essay_3"].map(str) + \
project_data["project_essay_4"].map(str)
In [32]:
approved_word_count = project_data[project_data['project_is_approved']==1]['essay'].str.split().apply(len)
approved_word_count = approved_word_count.values
rejected_word_count = project_data[project_data['project_is_approved']==0]['essay'].str.split().apply(len)
rejected_word_count = rejected_word_count.values
In [33]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_word_count, rejected_word_count])
plt.title('Words for each essay of the project')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Words in project essays')
plt.grid()
plt.show()
In [34]:
plt.figure(figsize=(10,3))
sns.distplot(approved_word_count, hist=False, label="Approved Projects")
sns.distplot(rejected_word_count, hist=False, label="Not Approved Projects")
plt.title('Words for each essay of the project')
plt.xlabel('Number of words in each eassay')
plt.legend()
plt.show()
Observation for Univariate analysis for project essay¶
- As per the count in Project essay, Project essay has as short as 200 words for the each essay.
- Box Plot- Seeing the box plot we can say like approved and rejected has both mean around 235 words. and rejected projects tends to have less words then approved projects.
- The project essay section.. we can say more elabroted eassy can lead to make it approved.
1.2.8 Univariate Analysis: Cost per project¶
In [35]:
# we get the cost of the project using resource.csv file
resource_data.head(2)
Out[35]:
In [36]:
# https://stackoverflow.com/questions/22407798/how-to-reset-a-dataframes-indexes-for-all-groups-in-one-step
price_data = resource_data.groupby('id').agg({'price':'sum', 'quantity':'sum'}).reset_index()
price_data.head(2)
Out[36]:
In [37]:
# join two dataframes in python:
project_data = pd.merge(project_data, price_data, on='id', how='left')
In [38]:
approved_price = project_data[project_data['project_is_approved']==1]['price'].values
rejected_price = project_data[project_data['project_is_approved']==0]['price'].values
In [39]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_price, rejected_price])
plt.title('Box Plots of Cost per approved and not approved Projects')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Price')
plt.grid()
plt.show()
In [40]:
plt.figure(figsize=(10,3))
sns.distplot(approved_price, hist=False, label="Approved Projects")
sns.distplot(rejected_price, hist=False, label="Not Approved Projects")
plt.title('Cost per approved and not approved Projects')
plt.xlabel('Cost of a project')
plt.legend()
plt.show()
In [41]:
# http://zetcode.com/python/prettytable/
from prettytable import PrettyTable
#If you get a ModuleNotFoundError error , install prettytable using: pip3 install prettytable
x = PrettyTable()
x.field_names = ["Percentile", "Approved Projects", "Not Approved Projects"]
for i in range(0,101,5):
x.add_row([i,np.round(np.percentile(approved_price,i), 3), np.round(np.percentile(rejected_price,i), 3)])
print(x)
Observation for Univariate analysis for project essay¶
- More then 95% of project tends to have less then 1000 dollar budget.
- 90% of approved Project budget comes under 500 dollar suprisingly 50% from max amount.
- Not Approved projects have slighlty heigher amount pitching then the approved projects. so we can say less budget project kinds have more chance to get approved.
1.2.9 Univariate Analysis: teacher_number_of_previously_posted_projects¶
1.2.9 Univariate Analysis: teacher_number_of_previously_posted_projects
Please do this on your own based on the data analysis that was done in the above cells
- teacher_number_of_previously_posted_projects = Number of project applications previously submitted by the same teacher. Example:
2
- Some Question we can answer
- What is the maximum number of projects teacher have ever posted
- What is the minimun number of projects teacher have posted
- What is the avg number of projects teacher are posting
- Which State teacher has posted most project
- and other...
In [42]:
univariate_barplots(project_data, 'teacher_number_of_previously_posted_projects', 'project_is_approved', top=50)
In [43]:
print("Max Projects submited By teacher: {} projects".format(project_data['teacher_number_of_previously_posted_projects'].max()))
print("Min Projects submited By teacher: {} projects".format(project_data['teacher_number_of_previously_posted_projects'].min()))
print('Avg Teacher has posted {} no of projects'.format(np.mean(project_data['teacher_number_of_previously_posted_projects'].values)))
In [44]:
# Does Previous porject count impact on Approving project
approved_projects = project_data[project_data['project_is_approved']==1]['teacher_number_of_previously_posted_projects'].values
rejected_projects = project_data[project_data['project_is_approved']==0]['teacher_number_of_previously_posted_projects'].values
print(np.mean(approved_projects))
print(np.mean(rejected_projects))
- It seems like whose project approved have more number of projects submited compare to whose not get approved.
In [45]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_projects, rejected_projects])
plt.title('Box Plots of Previous Project approved and not approved Projects')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Previous Project count')
plt.grid()
plt.show()
In [46]:
plt.figure(figsize=(10,3))
sns.kdeplot(approved_projects,label="Approved Projects", bw=0.6)
sns.kdeplot(rejected_projects,label="Not Approved Projects", bw=0.6)
plt.legend()
plt.show()
Observation for Univariate analysis for teacher_number_of_previously_posted_projects¶
- 82% people have publised their first project and it got approved.
- But who posted projects before have heigher rate as 96% of approve rate.
- As per the PDF and box plot Who has more number of approved projects have heigher chance for approving the project.
1.2.10 Univariate Analysis: project_resource_summary¶
1.2.10 Univariate Analysis: project_resource_summary
In [47]:
approved_project_res_summary = project_data[project_data['project_is_approved']==1]['project_resource_summary'].str.split().apply(len)
approved_project_res_summary = approved_project_res_summary.values
rejected_project_res_summary = project_data[project_data['project_is_approved']==0]['project_resource_summary'].str.split().apply(len)
rejected_project_res_summary = rejected_project_res_summary.values
In [48]:
# https://glowingpython.blogspot.com/2012/09/boxplot-with-matplotlib.html
plt.boxplot([approved_project_res_summary, rejected_project_res_summary])
plt.title('Words for each Project resource summary of the project')
plt.xticks([1,2],('Approved Projects','Rejected Projects'))
plt.ylabel('Words in project resource summary')
plt.grid()
plt.show()
In [49]:
plt.figure(figsize=(10,3))
sns.kdeplot(approved_project_res_summary,label="Approved Projects", bw=0.6)
sns.kdeplot(rejected_project_res_summary,label="Not Approved Projects", bw=0.6)
plt.legend()
plt.show()
Observation for Univariate analysis for project_resource_summary¶
- Approved projects has less word compare to rejected project means write summary but less.
- But there are 2 outlier means people got approed their projects by writing more words.
- It resemble both project essay and title outcome. For the begining level Approved projects have little heigher summary word count .
- As in Pdf there are 2 projects who break the linearly outcome of the graph.. which is Having more words still 2 project got approved.
Please do this on your own based on the data analysis that was done in the above cells
Check if the presence of the numerical digits in the project_resource_summary effects the acceptance of the project or not. If you observe that presence of the numerical digits is helpful in the classification, please include it for further process or you can ignore it.
In [50]:
# Google Search - how to check numeric value in string python
# https://stackoverflow.com/questions/19859282/check-if-a-string-contains-a-number
# After sometime of brainstroming i came up with one idea to add anoter column temporary one to just add the return type of hasnumber function
# so that .. for each row i will get true or false value with that and i can easily check numrical data can impact on aprroving the project or not.
# So i seached : Apply one function to one column and create the retrun of the function to new column in python panda df
# https://stackoverflow.com/a/19976286/6000190
def hasNumbers(inputString):
#return any(char.isdigit() for char in inputString)
if (any(char.isdigit() for char in inputString) == True ):
return 1
else :
return 0
project_data_temp = project_data
project_data_temp['project_resource_summary_has_numerical'] = np.vectorize(hasNumbers)(project_data_temp['project_resource_summary'])
In [51]:
#project_data[project_data['project_is_approved']==1]['project_resource_summary']
#
res_summ_with_num = project_data_temp.loc[(project_data_temp['project_is_approved'] == 1) & (project_data_temp['project_resource_summary_has_numerical'] == 1)]['project_resource_summary'].count()
res_summ_with_num_not_approved = project_data_temp.loc[(project_data_temp['project_is_approved'] == 1) & (project_data_temp['project_resource_summary_has_numerical'] == 0)]['project_resource_summary'].count()
print("Number of Project resource summary which has numerical value which has approved: {} ".format(res_summ_with_num))
print("Number of Project resource summary which has numerical value which has not approved: {} ".format(res_summ_with_num_not_approved))
#print(project_data_temp.loc[(project_data_temp['project_is_approved'] == 1) & (project_data_temp['project_resource_summary_has_numerical'] == "True")])
Looks like numerical values does matter when it comes to resourse summary.
1.3 Text preprocessing¶
1.3.1 Essay Text¶
In [52]:
project_data.head(2)
Out[52]:
In [53]:
# printing some random essays.
print(project_data['essay'].values[0])
print("="*50)
print(project_data['essay'].values[150])
print("="*50)
print(project_data['essay'].values[1000])
print("="*50)
print(project_data['essay'].values[20000])
print("="*50)
print(project_data['essay'].values[99999])
print("="*50)
In [54]:
# https://stackoverflow.com/a/47091490/4084039
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
In [55]:
sent = decontracted(project_data['essay'].values[20000])
print(sent)
print("="*50)
In [56]:
# \r \n \t remove from string python: http://texthandler.com/info/remove-line-breaks-python/
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
print(sent)
In [57]:
#remove spacial character: https://stackoverflow.com/a/5843547/4084039
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
print(sent)
In [58]:
# https://gist.github.com/sebleier/554280
# we are removing the words from the stop words list: 'no', 'nor', 'not'
stopwords= ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've",\
"you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \
'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their',\
'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', \
'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \
'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \
'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\
'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\
'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more',\
'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \
's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', \
've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn',\
"hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn',\
"mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", \
'won', "won't", 'wouldn', "wouldn't"]
In [59]:
# Combining all the above statemennts
from tqdm import tqdm
preprocessed_essays = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['essay'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_essays.append(sent.lower().strip())
In [60]:
# after preprocesing
preprocessed_essays[20000]
Out[60]:
1.3.2 Project title Tex¶
1.3.2 Project title Text
In [61]:
# similarly you can preprocess the titles also
# Processing steps for project title
# 1. Clean pharase
# 2. Remove String patterns
# 3. Remove Special charcter
# 4. Remove Stop words
# Combining all the above statemennts what used for essay
preprocessed_project_title = []
# tqdm is for printing the status bar
for sentance in tqdm(project_data['project_title'].values):
sent = decontracted(sentance)
sent = sent.replace('\\r', ' ')
sent = sent.replace('\\"', ' ')
sent = sent.replace('\\n', ' ')
sent = re.sub('[^A-Za-z0-9]+', ' ', sent)
# https://gist.github.com/sebleier/554280
sent = ' '.join(e for e in sent.split() if e not in stopwords)
preprocessed_project_title.append(sent.lower().strip())
In [62]:
# after preprocesing
preprocessed_project_title[20000]
Out[62]:
1. 4 Preparing data for models¶
In [63]:
project_data.columns
Out[63]:
we are going to consider
- school_state : categorical data
- clean_categories : categorical data
- clean_subcategories : categorical data
- project_grade_category : categorical data
- teacher_prefix : categorical data
- project_title : text data
- text : text data
- project_resource_summary: text data
- quantity : numerical
- teacher_number_of_previously_posted_projects : numerical
- price : numerical1.4.1 Vectorizing Categorical data¶
In [64]:
project_data.head(3)
Out[64]:
- One Hot Encoding : clean_categories
In [65]:
# we use count vectorizer to convert the values into one hot encoded features
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(vocabulary=list(sorted_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['clean_categories'].values)
print(vectorizer.get_feature_names())
categories_one_hot = vectorizer.transform(project_data['clean_categories'].values)
print("Shape of matrix after one hot encodig ",categories_one_hot.shape)
- One Hot Encoding : clean_subcategories
In [66]:
# we use count vectorizer to convert the values into one hot encoded features
vectorizer = CountVectorizer(vocabulary=list(sorted_sub_cat_dict.keys()), lowercase=False, binary=True)
vectorizer.fit(project_data['clean_subcategories'].values)
print(vectorizer.get_feature_names())
sub_categories_one_hot = vectorizer.transform(project_data['clean_subcategories'].values)
print("Shape of matrix after one hot encodig ",sub_categories_one_hot.shape)
- One Hot Encoding : State
In [67]:
my_counter = Counter()
for word in project_data['school_state'].values:
my_counter.update(word.split())
cat_dict = dict(my_counter)
sorted_school_state = dict(sorted(cat_dict.items(), key=lambda kv: kv[1]))
# we use count vectorizer to convert the values into one hot encoded features
vectorizer_state = CountVectorizer(vocabulary=list(sorted_school_state.keys()), lowercase=False, binary=True)
vectorizer_state.fit(project_data['school_state'].values)
print(vectorizer_state.get_feature_names())
school_state_one_hot = vectorizer_state.transform(project_data['school_state'].values)
print("Shape of matrix after one hot encodig ",school_state_one_hot.shape)
- One Hot Encoding : teacher_prefix
In [68]:
# How to remove nan from pyhton array
# https://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array
# I added cause it gives me error as float has no attribute as split but there was not float value init.
my_counter_techer = Counter()
project_data['teacher_prefix'] = project_data['teacher_prefix'].fillna(" ")
print(project_data['teacher_prefix'].unique())
for word in project_data['teacher_prefix'].values:
my_counter_techer.update(word.split())
cat_dict_techer = dict(my_counter_techer)
sorted_teacher_prefix = dict(sorted(cat_dict_techer.items(), key=lambda kv: kv[1]))
# we use count vectorizer to convert the values into one hot encoded features
vectorizer_teacher = CountVectorizer(vocabulary=list(sorted_teacher_prefix.keys()), lowercase=False, binary=True)
vectorizer_teacher.fit(project_data['teacher_prefix'].values )
print(vectorizer_teacher.get_feature_names())
teacher_prefix_one_hot = vectorizer_teacher.transform(project_data['teacher_prefix'].values)
print("Shape of matrix after one hot encodig ",teacher_prefix_one_hot.shape)
- One Hot Encoding : project_grade_category
In [138]:
# How to remove nan from pyhton array
# https://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array
# I added cause it gives me error as float has no attribute as split but there was not float value init.
# project_data['project_grade_category'] = project_data['project_grade_category'].fillna(" ")
print(project_data['project_grade_category'].unique())
my_counter_project_grade = Counter()
for word in project_data['project_grade_category'].values:
my_counter_project_grade.update(word.split(","))
cat_dict_procat = dict(my_counter_project_grade)
sorted_procat = dict(sorted(cat_dict_procat.items(), key=lambda kv: kv[1]))
# we use count vectorizer to convert the values into one hot encoded features
vectorizer_teacher = CountVectorizer(vocabulary=list(sorted_procat.keys()), lowercase=False, binary=True)
vectorizer_teacher.fit(project_data['project_grade_category'].values )
print(vectorizer_teacher.get_feature_names())
project_cat_one_hot = vectorizer_teacher.transform(project_data['project_grade_category'].values)
print("Shape of matrix after one hot encodig ",project_cat_one_hot.shape)
In [70]:
# Please do the similar feature encoding with state, teacher_prefix and project_grade_category also
1.4.2 Vectorizing Text data¶
1.4.2.1 Bag of words¶
In [71]:
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
text_bow = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_bow.shape)
1.4.2.2 Bag of Words on `project_title`
In [72]:
# you can vectorize the title also
# before you vectorize the title make sure you preprocess it
# We are considering only the words which appeared in at least 10 documents(rows or projects).
vectorizer = CountVectorizer(min_df=10)
text_bow_title = vectorizer.fit_transform(preprocessed_project_title)
print("Shape of matrix after one hot encodig ",text_bow_title.shape)
In [73]:
# Similarly you can vectorize for title also
1.4.2.3 TFIDF vectorizer¶
In [74]:
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
text_tfidf = vectorizer.fit_transform(preprocessed_essays)
print("Shape of matrix after one hot encodig ",text_tfidf.shape)
1.4.2.4 TFIDF Vectorizer on `project_title`
In [75]:
# Similarly you can vectorize for title also
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=10)
text_tfidf_title = vectorizer.fit_transform(preprocessed_project_title)
print("Shape of matrix after one hot encodig ",text_tfidf_title.shape)
1.4.2.5 Using Pretrained Models: Avg W2V¶
In [76]:
'''
# Reading glove vectors in python: https://stackoverflow.com/a/38230349/4084039
def loadGloveModel(gloveFile):
print ("Loading Glove Model")
f = open(gloveFile,'r', encoding="utf8")
model = {}
for line in tqdm(f):
splitLine = line.split()
word = splitLine[0]
embedding = np.array([float(val) for val in splitLine[1:]])
model[word] = embedding
print ("Done.",len(model)," words loaded!")
return model
model = loadGloveModel('glove.42B.300d.txt')
# ============================
Output:
Loading Glove Model
1917495it [06:32, 4879.69it/s]
Done. 1917495 words loaded!
# ============================
words = []
for i in preproced_texts:
words.extend(i.split(' '))
for i in preproced_titles:
words.extend(i.split(' '))
print("all the words in the coupus", len(words))
words = set(words)
print("the unique words in the coupus", len(words))
inter_words = set(model.keys()).intersection(words)
print("The number of words that are present in both glove vectors and our coupus", \
len(inter_words),"(",np.round(len(inter_words)/len(words)*100,3),"%)")
words_courpus = {}
words_glove = set(model.keys())
for i in words:
if i in words_glove:
words_courpus[i] = model[i]
print("word 2 vec length", len(words_courpus))
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
import pickle
with open('glove_vectors', 'wb') as f:
pickle.dump(words_courpus, f)
'''
Out[76]:
In [77]:
# stronging variables into pickle files python: http://www.jessicayung.com/how-to-use-pickle-to-save-and-load-variables-in-python/
# make sure you have the glove_vectors file
#https://drive.google.com/open?id=14nf-h6aYdhL_01I8DVg9CFZ5aMqAXeTi
with open('glove_vectors', 'rb') as f:
model = pickle.load(f)
glove_words = set(model.keys())
In [78]:
# average Word2Vec
# compute average word2vec for each review.
avg_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
avg_w2v_vectors.append(vector)
print(len(avg_w2v_vectors))
print(len(avg_w2v_vectors[0]))
1.4.2.6 Using Pretrained Models: AVG W2V on `project_title`
In [79]:
# Similarly you can vectorize for title also
# compute average word2vec for each project title
avg_w2v_vectors_project_title = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_project_title): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
cnt_words =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if word in glove_words:
vector += model[word]
cnt_words += 1
if cnt_words != 0:
vector /= cnt_words
avg_w2v_vectors_project_title.append(vector)
print(len(avg_w2v_vectors_project_title))
print(len(avg_w2v_vectors_project_title[0]))
1.4.2.7 Using Pretrained Models: TFIDF weighted W2V¶
In [80]:
# S = ["abc def pqr", "def def def abc", "pqr pqr def"]
tfidf_model = TfidfVectorizer()
tfidf_model.fit(preprocessed_essays)
# we are converting a dictionary with word as a key, and the idf as a value
dictionary = dict(zip(tfidf_model.get_feature_names(), list(tfidf_model.idf_)))
tfidf_words = set(tfidf_model.get_feature_names())
In [81]:
# average Word2Vec
# compute average word2vec for each review.
tfidf_w2v_vectors = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_essays): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
tfidf_w2v_vectors.append(vector)
print(len(tfidf_w2v_vectors))
print(len(tfidf_w2v_vectors[0]))
1.4.2.9 Using Pretrained Models: TFIDF weighted W2V on `project_title`
In [82]:
# Similarly you can vectorize for title also
tfidf_w2v_vectors_project_title = []; # the avg-w2v for each sentence/review is stored in this list
for sentence in tqdm(preprocessed_project_title): # for each review/sentence
vector = np.zeros(300) # as word vectors are of zero length
tf_idf_weight =0; # num of words with a valid vector in the sentence/review
for word in sentence.split(): # for each word in a review/sentence
if (word in glove_words) and (word in tfidf_words):
vec = model[word] # getting the vector for each word
# here we are multiplying idf value(dictionary[word]) and the tf value((sentence.count(word)/len(sentence.split())))
tf_idf = dictionary[word]*(sentence.count(word)/len(sentence.split())) # getting the tfidf value for each word
vector += (vec * tf_idf) # calculating tfidf weighted w2v
tf_idf_weight += tf_idf
if tf_idf_weight != 0:
vector /= tf_idf_weight
tfidf_w2v_vectors_project_title.append(vector)
print(len(tfidf_w2v_vectors_project_title))
print(len(tfidf_w2v_vectors_project_title[0]))
1.4.3 Vectorizing Numerical features¶
In [83]:
# check this one: https://www.youtube.com/watch?v=0HOqOcln3Z4&t=530s
# standardization sklearn: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
from sklearn.preprocessing import StandardScaler
# price_standardized = standardScalar.fit(project_data['price'].values)
# this will rise the error
# ValueError: Expected 2D array, got 1D array instead: array=[725.05 213.03 329. ... 399. 287.73 5.5 ].
# Reshape your data either using array.reshape(-1, 1)
price_scalar = StandardScaler()
price_scalar.fit(project_data['price'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {price_scalar.mean_[0]}, Standard deviation : {np.sqrt(price_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
price_standardized = price_scalar.transform(project_data['price'].values.reshape(-1, 1))
In [84]:
price_standardized
Out[84]:
In [136]:
# teacher_number_of_previously_posted_projects
teacher_number_of_previously_posted_projects_scalar = StandardScaler()
teacher_number_of_previously_posted_projects_scalar.fit(project_data['teacher_number_of_previously_posted_projects'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {teacher_number_of_previously_posted_projects_scalar.mean_[0]}, Standard deviation : {np.sqrt(teacher_number_of_previously_posted_projects_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
teacher_number_of_previously_posted_projects_standardized = teacher_number_of_previously_posted_projects_scalar.transform(project_data['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1))
print(teacher_number_of_previously_posted_projects_standardized)
In [137]:
# quantity
quantity_scalar = StandardScaler()
quantity_scalar.fit(project_data['quantity'].values.reshape(-1,1)) # finding the mean and standard deviation of this data
print(f"Mean : {quantity_scalar.mean_[0]}, Standard deviation : {np.sqrt(quantity_scalar.var_[0])}")
# Now standardize the data with above maen and variance.
quantity_standardized = quantity_scalar.transform(project_data['quantity'].values.reshape(-1, 1))
print(quantity_standardized)
1.4.4 Merging all the above features¶
- we need to merge all the numerical vectors i.e catogorical, text, numerical vectors
In [85]:
print(categories_one_hot.shape)
print(sub_categories_one_hot.shape)
print(text_bow.shape)
print(price_standardized.shape)
In [97]:
# merge two sparse matrices: https://stackoverflow.com/a/19710648/4084039
from scipy.sparse import hstack
# with the same hstack function we are concatinating a sparse matrix and a dense matirx :)
X = hstack((categories_one_hot, sub_categories_one_hot, text_bow_title, price_standardized))
X.shape
Out[97]:
Assignment 2: Apply TSNE¶
Assignment 2: Apply TSNE
If you are using any code snippet from the internet, you have to provide the reference/citations, as we did in the above cells. Otherwise, it will be treated as plagiarism without citations.
- In the above cells we have plotted and analyzed many features. Please observe the plots and write the observations in markdown cells below every plot.
- EDA: Please complete the analysis of the feature: teacher_number_of_previously_posted_projects
-
- Build the data matrix using these features
- school_state : categorical data (one hot encoding)
- clean_categories : categorical data (one hot encoding)
- clean_subcategories : categorical data (one hot encoding)
- teacher_prefix : categorical data (one hot encoding)
- project_grade_category : categorical data (one hot encoding)
- project_title : text data (BOW, TFIDF, AVG W2V, TFIDF W2V)
- price : numerical
- teacher_number_of_previously_posted_projects : numerical
- Now, plot FOUR t-SNE plots with each of these feature sets.
- categorical, numerical features + project_title(BOW)
- categorical, numerical features + project_title(TFIDF)
- categorical, numerical features + project_title(AVG W2V)
- categorical, numerical features + project_title(TFIDF W2V)
- Concatenate all the features and Apply TNSE on the final data matrix
- Note 1: The TSNE accepts only dense matrices
- Note 2: Consider only 5k to 6k data points to avoid memory issues. If you run into memory error issues, reduce the number of data points but clearly state the number of datat-poins you are using
In [87]:
# this is the example code for TSNE
import numpy as np
from sklearn.manifold import TSNE
from sklearn import datasets
import pandas as pd
import matplotlib.pyplot as plt
iris = datasets.load_iris()
x = iris['data']
y = iris['target']
print(y)
tsne = TSNE(n_components=2, perplexity=30, learning_rate=200)
print(x.shape)
X_embedding = tsne.fit_transform(x)
# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix
for_tsne = np.hstack((X_embedding, y.reshape(-1,1)))
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dimension_x','Dimension_y','Score'])
colors = {0:'red', 1:'blue', 2:'green'}
plt.scatter(for_tsne_df['Dimension_x'], for_tsne_df['Dimension_y'], c=for_tsne_df['Score'].apply(lambda x: colors[x]))
plt.show()
2.1 TSNE with `BOW` encoding of `project_title` feature
In [123]:
# please write all of the code with proper documentation and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
X = hstack((categories_one_hot,
sub_categories_one_hot,
school_state_one_hot,
teacher_prefix_one_hot,
project_cat_one_hot,
text_bow_title,
price_standardized,
quantity_standardized,
teacher_number_of_previously_posted_projects_standardized))
projectT_1000 = X.toarray()
x = projectT_1000[0:1000,:]
y = project_data['project_is_approved'][0:1000]
tsne = TSNE(n_components=2, random_state=0, perplexity=30, learning_rate=1000)
#X_embedding = tsne.fit_transform(text_bow_title.toarray()[0:1000,:])
X_embedding = tsne.fit_transform(x)
# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix
# creating a new dataframe which will help us for plotting data
for_tsne = np.vstack((X_embedding.T, y)).T
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dim_1','Dim_2','label'])
# Ploting the result of tsne
sns.FacetGrid(for_tsne_df, hue="label", height=6).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()
plt.title("TSNE with `BOW` encoding of `project_title` feature ")
plt.show()
- Here i have taken 1000 data points from dataset with perplexity 50 and learning rate 1000.
- Here Its making small small clusters of bow encoding of project title features.
2.2 TSNE with `TFIDF` encoding of `project_title` feature
In [130]:
# please write all of the code with proper documentation and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
X = hstack((categories_one_hot,
sub_categories_one_hot,
school_state_one_hot,
teacher_prefix_one_hot,
project_cat_one_hot,
text_tfidf_title,
price_standardized,
quantity_standardized,
teacher_number_of_previously_posted_projects_standardized))
projectT_1000 = X.toarray()
x = projectT_1000[0:1000,:]
y = project_data['project_is_approved'][0:1000]
tsne = TSNE(n_components=2, perplexity=80, learning_rate=1000)
#print(y)
#X_embedding = tsne.fit_transform(text_bow_title.toarray())
X_embedding = tsne.fit_transform(x)
# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix
# creating a new dataframe which will help us for plotting data
for_tsne = np.vstack((X_embedding.T, y)).T
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dim_1','Dim_2','label'])
# Ploting the result of tsne
sns.FacetGrid(for_tsne_df, hue="label", height=6).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()
plt.title("TSNE with `AVG W2V` encoding of `project_title` feature")
plt.show()
- This is data with tfidf with project title
- Here also we can clusters of reducted dimensions of data.
- Center has more bigger cluster points init.
2.3 TSNE with `AVG W2V` encoding of `project_title` feature
In [133]:
# please write all of the code with proper documentation and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
X = hstack((categories_one_hot,
sub_categories_one_hot,
school_state_one_hot,
teacher_prefix_one_hot,
project_cat_one_hot,
avg_w2v_vectors_project_title,
price_standardized,
quantity_standardized,
teacher_number_of_previously_posted_projects_standardized))
projectT_1000 = X.toarray()
x = projectT_1000[0:1000,:]
y = project_data['project_is_approved'][0:1000]
tsne = TSNE(n_components=2, random_state=0, perplexity=30, learning_rate=1000)
#print(y)
#X_embedding = tsne.fit_transform(text_bow_title.toarray())
X_embedding = tsne.fit_transform(x)
# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix
# creating a new dataframe which will help us for plotting data
for_tsne = np.vstack((X_embedding.T, y)).T
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dim_1','Dim_2','label'])
# Ploting the result of tsne
sns.FacetGrid(for_tsne_df, hue="label", height=6).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()
plt.title("TSNE with `AVG W2V` encoding of `project_title` feature")
plt.show()
- This is data with avg w2v encoded project title features
- No clusters here.
2.4 TSNE with `TFIDF Weighted W2V` encoding of `project_title` feature
In [132]:
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
X = hstack((categories_one_hot,
sub_categories_one_hot,
school_state_one_hot,
teacher_prefix_one_hot,
project_cat_one_hot,
tfidf_w2v_vectors_project_title,
price_standardized,
quantity_standardized,
teacher_number_of_previously_posted_projects_standardized))
projectT_1000 = X.toarray()
x = projectT_1000[0:1000,:]
y = project_data['project_is_approved'][0:1000]
tsne = TSNE(n_components=2, perplexity=30, learning_rate=2000)
#print(y)
#X_embedding = tsne.fit_transform(text_bow_title.toarray())
X_embedding = tsne.fit_transform(x)
# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix
# creating a new dataframe which will help us for plotting data
for_tsne = np.vstack((X_embedding.T, y)).T
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dim_1','Dim_2','label'])
# Ploting the result of tsne
sns.FacetGrid(for_tsne_df, hue="label", height=6).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()
plt.title("TSNE with `TFIDF Weighted W2V` encoding of `project_title` feature ")
plt.show()
- Here also we can clusters of reducted dimensions of data.
In [135]:
# Write few sentences about the results that you obtained and the observations you made.
# please write all the code with proper documentation, and proper titles for each subsection
# when you plot any graph make sure you use
# a. Title, that describes your plot, this will be very helpful to the reader
# b. Legends if needed
# c. X-axis label
# d. Y-axis label
X = hstack((categories_one_hot,
sub_categories_one_hot,
school_state_one_hot,
teacher_prefix_one_hot,
project_cat_one_hot,
text_bow_title,
text_tfidf_title,
avg_w2v_vectors_project_title,
tfidf_w2v_vectors_project_title,
price_standardized,
quantity_standardized,
teacher_number_of_previously_posted_projects_standardized))
projectT_1000 = X.toarray()
x = projectT_1000[0:1000,:]
y = project_data['project_is_approved'][0:1000]
tsne = TSNE(n_components=2, perplexity=50, learning_rate=1000)
#print(y)
#X_embedding = tsne.fit_transform(text_bow_title.toarray())
X_embedding = tsne.fit_transform(x)
# if x is a sparse matrix you need to pass it as X_embedding = tsne.fit_transform(x.toarray()) , .toarray() will convert the sparse matrix into dense matrix
# creating a new dataframe which will help us for plotting data
for_tsne = np.vstack((X_embedding.T, y)).T
for_tsne_df = pd.DataFrame(data=for_tsne, columns=['Dim_1','Dim_2','label'])
# Ploting the result of tsne
sns.FacetGrid(for_tsne_df, hue="label", height=6).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()
plt.title("all the features and Apply TNSE on the final data matrix")
plt.show()
2.5 Summary
Observations¶
- There are 85% of projects are approved.
- If you have 4 to 6 words of Project title then it can be more suatible for approving.
- Those teacher has before submitions tends to have higher approving rate.
- Less budget project leads to project apprval.
- Above 80% teacher who submited their first project got approved.
- Try to mention numerical points to convey your resource requiremnet.
- Mrs. prefix persons has highest approve rates then other.
- More project got submitted on Literacy categories.